Supervisório Inteligente de Processos na Idústria do Petróleo e Gás :Aplicação em uma Coluna de Destilação Simulada Integrada a Instrumentação Real

The need to implement a software architecture that promotes the development of a SCADA supervisory system for monitoring industrial processes simulated with the flexibility of adding intelligent modules and devices such as CLP, according to the specifications of the problem, it was the motivation for this work. In the present study, we developed an intelligent supervisory system on a simulation of a distillation column modeled with Unisim. Furthermore, OLE Automation was used as communication between the supervisory and simulation software, which, with the use of the database, promoted an architecture both scalable and easy to maintain. Moreover, intelligent modules have been developed for preprocessing, data characteristics extraction, and variables inference. These modules were fundamentally based on the Encog software

Um sistema inteligente de classificação de sinais de EEG para Interface Cérebro-Computador

The Brain-Computer Interfaces (BCI) have as main purpose to establish a communication path with the central nervous system (CNS) independently from the standard pathway (nervous, muscles), aiming to control a device. The main objective of the current research is to develop an off-line BCI that separates the different EEG patterns resulting from strictly mental tasks performed by an experimental subject, comparing the effectiveness of different signal-preprocessing approaches. We also tested different classification approaches: all versus all, one versus one and a hierarchic classification approach. No preprocessing techniques were found able to improve the system performance. Furthermore, the hierarchic approach proved to be capable to produce results above the expected by literature

Controle Robusto Aplicado a Conversor Buck-Boost em Sistemas Fotovoltaicos

The need to implement a software architecture that promotes the development of a SCADA supervisory system for monitoring industrial processes simulated with the flexibility of adding intelligent modules and devices such as CLP, according to the specifications of the problem, it was the motivation for this work. In the present study, we developed an intelligent supervisory system on a simulation of a distillation column modeled with Unisim. Furthermore, OLE Automation was used as communication between the supervisory and simulation software, which, with the use of the database, promoted an architecture both scalable and easy to maintain. Moreover, intelligent modules have been developed for preprocessing, data characteristics extraction, and variables inference. These modules were fundamentally based on the Encog software

Detecção e diagnostico de agarramento em válvulas posicionadoras

Valve stiction, or static friction, in control loops is a common problem in modern industrial processes. Recently, many studies have been developed to understand, reproduce and detect such problem, but quantification still remains a challenge. Since the valve position (mv) is normally unknown in an industrial process, the main challenge is to diagnose stiction knowing only the output signals of the process (pv) and the control signal (op). This paper presents an Artificial Neural Network approach in order to detect and quantify the amount of static friction using only the pv and op information. Different methods for preprocessing the training set of the neural network are presented. Those methods are based on the calculation of centroid and Fourier Transform. The proposal is validated using a simulated process and the results show a satisfactory measurement of stiction.